Meridian Edge + Streamlit

import streamlit as st
import requests
import pandas as pd
import time

API_KEY = "me_live_YOUR_KEY"
HEADERS = {"X-API-Key": API_KEY}

st.set_page_config(page_title="Prediction Market Consensus", layout="wide")
st.title("Prediction Market Consensus")

sport = st.selectbox("Sport", ["NBA", "NHL", "MLB", "MLS"])
auto_refresh = st.checkbox("Auto-refresh (30s)", value=True)

@st.cache_data(ttl=30)
def fetch_consensus(sport):
    r = requests.get(
        "https://meridianedge.io/api/v1/consensus",
        headers=HEADERS,
        params={"sport": sport, "limit": 20}
    )
    return r.json().get("events", [])

events = fetch_consensus(sport)
df = pd.DataFrame(events)

if not df.empty:
    df["consensus_%"] = (df["consensus_prob"] * 100).round(1)
    df["divergence_%"] = (df.get("divergence_pct", 0) * 100).round(1)
    st.dataframe(
        df[["title", "consensus_%", "divergence_%", "market_count"]],
        use_container_width=True
    )

if auto_refresh:
    time.sleep(30)
    st.rerun()